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Edi Sutoyo
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journalijadis@gmail.com
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Indonesian Scientific Journal (Jurnal Ilmiah Indonesia) Jl. Pasar Atas No 3, Kompleks Setramas Kota Cimahi, Bandung
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International Journal of Advances in Data and Information Systems
Published by Indonesian Scientific Journal
ISSN : -     EISSN : 27213056     DOI : https://doi.org/10.25008/ijadis
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Advances in Data and Information Systems (IJADIS) (e-ISSN: 2721-3056) is a peer-reviewed journal in the field of data science and information system that is published twice a year; scheduled in April and October. The journal is published for those who wish to share information about their research and innovations and for those who want to know the latest results in the field of Data Science and Information System. The Journal is published by the Indonesian Scientific Journal. Accepted paper will be available online (free access), and there will be no publication fee. The author will get their own personal copy of the paperwork. IJADIS welcomes all topics that are relevant to data science, and information system. The listed topics of interest are as follows: Data clustering and classifications Statistical model in data science Artificial intelligence and machine learning in data science Data visualization Data mining Data intelligence Business intelligence and data warehousing Cloud computing for Big Data Data processing and analytics in IoT Tools and applications in data science Vision and future directions of data science Computational Linguistics Text Classification Language resources Information retrieval Information extraction Information security Machine translation Sentiment analysis Semantics Summarization Speech processing Mathematical linguistics NLP applications Information Science Cryptography and steganography Digital Forensic Social media and social network Crowdsourcing Computational intelligence Collective intelligence Graph theory and computation Network science Modeling and simulation Parallel and distributed computing High-performance computing Information architecture
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 8 Documents
 1 
Search results for , issue "Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems" : 8 Documents clear
Data-Driven Analytical Model Using Machine Learning Algorithms: A Case Study on Clean and Healthy Living Behaviour in Surabaya City's Coastal Areas Harun Al Azies; Noval Ariyanto; Ishak Bintang Dikaputra
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1309

Abstract

The objective of this article is to use machine learning technology, specifically the Support Vector Machine (SVM) approach with a linear kernel, to analyze and predict clean and healthy living behavior (CHLB) in coastal dwellings in Surabaya City. To train the SVM model, researchers collect health and environmental data from the region. As a result, our model predicts house CHLB status with an 83% accuracy rate. The most important variables in this prediction are the amount of community access to appropriate sanitary facilities, the health of households, and the sustainability of public areas that meet health requirements. These findings have crucial implications for attempts to improve CHLB in Surabaya's coastal areas in compliance with the National Medium-Term Development Plan (RPJMN) aims. Furthermore, the findings of this study can be used to build more targeted and long-term health policies in coastal communities.
Classification of Students' Academic Performance Using Neural Network and C4.5 Model Sulika Sulika; Ririen Kusumawati; Yunifa Miftachul Arif
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1311

Abstract

ducation involves deliberately creating an environment and learning process to empower students to fully utilize their academic and non-academic potential. It encompasses fostering spiritual qualities, religious understanding, self-discipline, cognitive abilities, and skills necessary for personal, societal, national, and state development. Madrasah Aliyah, in particular, emphasizes preparing participants for higher studies in areas of their interest, thereby showcasing their academic prowess. The evaluation of educational models like Neural Networks is crucial for ensuring their effectiveness in problem-solving. This involves testing and assessing the performance of the Neural Network model to ensure its accuracy and reliability. Similarly, the C4.5 method, based on condition data mining, is utilized to measure classification performance by assessing accuracy, precision, and recall. Research findings indicate that the neural network algorithm is more adept at accurately classifying students' academic abilities compared to the C4.5 algorithm. With an accuracy of 92.6% for the neural network algorithm and 80.6% for the C4.5 algorithm, it is evident that the former is more precise in determining the classification of students' academic abilities. This highlights the suitability of the neural network approach for classifying academic abilities in Madrasah Aliyah. Furthermore, the insights gained from this classification process can be extrapolated to benefit other madrasas.
Integrated Multi-Income Stream Performance Dashboard: a Japanese Corporate Banking Case Krisnhu Hananta Rachansa; Wasesa Meditya
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1313

Abstract

In response to the complex operational challenges faced by Japanese Corporate Banking (JCB), arising from the coexistence of disparate core banking systems post-merger, this study aims to address inherent issues affecting marketing performance monitoring. The existing condition at JCB is characterized by data inconsistency, limited system interoperability, and fragmented income tracking through multiple Excel reports and management systems. Recognizing the gaps in the current setup, the research question revolves around how to enhance marketing performance monitoring effectively. The research objectives, therefore, encompass the development and implementation of a tailored integrated report utilizing the CRISP-DM methodology. This innovative performance dashboard harmoniously consolidates data from diverse sources, presenting a cohesive representation crucial for comprehensive marketing performance assessment. Leveraging advanced methodologies like data normalization and cross-platform integration, the research approach ensures streamlined income tracking, mitigating existing limitations. The data, drawn from various product applications, undergoes meticulous processing to facilitate a unified view on the integrated dashboard. The anticipated result is a significant improvement in monitoring efficiency, heightened data accuracy, and an empowered decision-making process within JCB's operations. The business implication of this initiative is the tangible enhancement of the bank's ability to comprehensively assess income performance, thereby elevating the quality of strategic decision-making and reinforcing JCB's competitive positioning in the banking sector.
Managing Inherent IT Business Risk against Cyber Threats: a Decision Analysis Case Study of an Oil and Gas Company I Wayan Novit Marhaendra Putra; Meditya Wasesa
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1315

Abstract

XYZ, an anonymized oil and gas company, aims to enhance cyber resilience by strategically managing inherent risk profiles in cybersecurity, aligned with business needs and stakeholder expectations. This research addresses challenges including Information Security Control determination, proficiency improvement in risk management, and ISMS preparedness. Additionally, it tackles procurement strategy for Security Operations Control across XYZ Group, operating under PSC Gross Split, Cost Recovery, and Non-PSC statuses. Utilizing diverse frameworks such as problem tree analysis, stakeholders’ power-interest matrix, MITRE ATT&CK, NIST 800-53, COBIT 2019, ISO 27005:2022, KAMI 5.0, and SMART, data analysis includes risk documents, interviews, and cyber-attack data. The research establishes effective IS Control for risk mitigation, readiness for Information Security Management System ISMS implementation, strategic programs enhancing risk management capability, and refined Security Operations Control procurement. These outcomes, incorporated into a collaborative contract structure, significantly mitigate cyber threats and potential impacts, such as disruptions to operations, revenue reduction, increased costs, data theft, and non-compliance.
Recommendation of Prospective Construction Service Providers in Government Procurement Using Decision Tree Eva Yustina; Mokhamad Amin Hariyadi; Cahyo Crysdian
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1316

Abstract

The determination of prospective construction service providers using the direct procurement method is the authority of the Goods/ Services Procurement Officer. Administrative requirements are an important factor in selecting prospective construction service providers. The use of the decision tree method in this study is to find out, determine, and analyse the variables that influence the assessment of the feasibility of prospective construction service providers, and get an accuracy value in providing an assessment of the feasibility of prospective construction service providers. The data used in this study are 153 datasets consisting of 13 variables. The existing variables are divided into basic variables and additional variables. The basic variables consist of 5 variables, namely experts, work experience, quality of work, winning tenders and contract value. While the additional variables consist of 8 variables namely business entity status, business entity form, business entity NPWP, business entity domicile, business entity qualification, type of business licence, percentage of work and construction services business licence. By using the decision tree method, the accuracy on the basic variable is 84.84%. The addition of additional variables to the basic variables resulted in an accuracy of 90.91%. This shows that by adding additional variables the accuracy results are higher than using only the basic variables.
Recommendation System for Selecting Web Programming Learning Materials for Vocational High School Students using Multi-criteria Recommendation Systems Lia Wahyuliningtyas; Yunifa Mittachul Arif; Ririen Kusumawati
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1317

Abstract

In the independent curriculum, the learning that is carried out focuses on developing character, student competence and honing interests, talents. So the amount of learning material given to students does not have to be complete or less. Apart from that, the independent curriculum no longer burdens students with achieving a minimum score because assessments no longer use Minimum Completeness Criteria (KKM) scores. This makes it difficult for teachers to determine whether the material that has been explained can be understood because grades are not a benchmark for a student's success. In fact, if the teacher does not know a student's understanding, the teacher will have difficulty continuing to the next material. Implementation of the Multi-Criteria Recommender System (MCRS) can make it easier for teachers to predict whether students can progress to the next material and recommend which modules are suitable for these students. The recommendation system that will be built is in the form of web-based learning media so that students can be more interested and can help teachers improve learning outcomes. The method used is collaborative filtering by comparing adjusted cosine similarity, cosine based similarity and spearman rank order correlation. Based on the implementation of MCRS using the collaborative filtering method, it shows that the results of the recommendation system have a good impact on the teaching and learning process. Based on the 3 algorithms implemented, the best prediction result is cosine based similarity because the MAE value obtained is the lowest, namely 1.19 and the accuracy value is 76%.
Exploring Sentiment Trends: Deep Learning Analysis of Social Media Reviews on Google Play Store by Netizens Rosa Eliviani; Dwi Diana Wazaumi
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1318

Abstract

This study explores sentiment analysis of Instagram app reviews using Long Short-Term Memory (LSTM) algorithms. The rise of app stores has transformed digital interactions, particularly for social media apps. Leveraging LSTM, we aim to understand user sentiments expressed in Instagram application reviews, offering insights to enhance user experience and address concerns. The methodology involves data crawling, preprocessing, LSTM model training, and evaluation metrics. Our findings reveal promising results in accurately identifying user sentiments, with an accuracy of 77.77%, precision of 0.45, recall of 0.089, and F1-score of 0.15. This study underscores the importance of sentiment analysis in understanding user feedback and its implications for app development and user engagement.
Sentiment Analysis of the Sheikh Zayed Grand Mosque’s Visitor Reviews on Google Maps Using the VADER Method Elinda Elinda; Herman Yuliansyah; Muhammad Iqbal Abu Latiffi
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1320

Abstract

The Sheikh Zayed Grand Mosque in Solo is a replica of the Zayed Grand Mosque in Abu Dhabi. Many people have provided reviews on Google Maps after visiting the mosque. This research aims to determine the sentiment results regarding visitors’ reviews by developing a sentiment analysis model using a combination of the Valance Aware Dictionary for Sentiment Reasoning (VADER) and Deep Translator methods. This research was conducted in two phases. The first phase proposed a sentiment analysis model using VADER and Deep-Translator with public datasets. Later, the resulting sentiment analysis model was applied in the second phase to analyze the dataset of mosque visitor reviews and determine public perceptions. This research compares two preprocessing models (PPTV1 and PPTV2) and continues with the translation and sentiment prediction processes. The evaluation results show the proposed model (PPTV2) achieved the best average accuracy values of 72%, precision of 83%, recall of 72%, and F1-Score of 75% for the three examined datasets. The results of visitor review sentiment obtained showed 83.3% positive, 9.5% neutral, and 7.2% negative. The analysis findings show that people are amazed by the beauty and majesty of the mosque. However, some people provide negative reviews of the mosque’s facilities.

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